Machine vision systems (also termed “vision systems”) that perform measurement, inspection, alignment of objects and/or decoding of symbology (e.g. bar codes) are used in a wide range of applications and industries. These systems are based around the use of an image sensor, which acquires images (typically grayscale or color, and in one, two or three dimensions) of the subject or object, and processes these acquired images using an on-board or remote, interconnected vision system processor. The processor generally includes both processing hardware and non-transitory computer-readable program instructions that perform one or more vision system processes to generate a desired output based upon the image's processed information. This image information is typically provided within an array of image pixels each having various colors and/or intensities. In the example of a symbology (barcode) reader, the user or automated process acquires an image of an object that is believed to contain one or more barcodes. The image is processed to identify barcode features, which are then decoded by a decoding process and/or processor obtain the inherent alphanumeric data represented by the code. In other types of vision systems, various vision system tools (e.g. edge detectors, calipers, blob analysis) are employed by the system processor to detect edges and other features that allow for recognition of object features, and the determination of desired information based upon these features—for example whether the object is defective or whether it is properly aligned. Likewise, vision system tools can be used to detect imperfections and/or defects in an object—such as in a surface inspection arrangement.
In a vision system, a major component is the vision system camera assembly. The camera assembly typically includes a lens (optics) and an imager (or “sensor”) that provides the array of image pixel information. The vision system processor receives the pixel data from the imager/sensor and processes it to derive useful vision system information about the imaged scene and/or object. The vision system processor and related components (e.g. data memory, decoders, etc.) can be provided within the camera assembly's physical housing or enclosure, or some or all of these vision processing components can be mounted remotely (e.g. within a PC, or other remote, self-contained processing system), and linked by a wired or wireless interconnect.
The camera assembly can be arranged to direct light from the scene through an optic that focuses the light on either a 2D image sensor or a 1D image sensor. A 2D arrangement typically employs a cylindrical barrel lens arrangement, or the like, to focus the light onto an array of sensor pixels arranged in a grid of N×M (e.g. height/width), while a 1D arrangement, also termed a “line scan” camera includes a sensor arranged as a single row of N pixels arranged at an appropriate width. Image acquisition is in the form of a “line” or “row” of pixels, with the acquisition of each row typically synchronized by an encoder or other motion-sensing device that registers a series of acquired lines. Thus, the image is typically acquired along a width direction that is transverse to the motion direction. The overall image of the surface comprises a continuous grouping of line images registered to a particular length increment based upon the motion of the surface with respect to the camera. Note that either, or both, the camera and imaged surface can be in motion (i.e. providing “relative” motion with respect to each other).
In various line-scan environments, as well as other imaging environments—for example those employing a 2D image sensor—the vision system is deployed in an environment that can be relatively harsh. For example, the environment can include airborne dust, chips, vapors (e.g. paint, ink, steam, etc.) and other contaminants that can obscure the image or accrete to the imager optics. Such occluding debris can temporarily or permanently (i.e. until it is cleaned from the optics) degrade or even blind the vision system.